Detecting contextual collective anomalies at a Glance
Mario Alfonso Prado-Romero, Andrés Gago-Alonso · 2016
Many phenomena in our world can be modeled as networks, from neurons in the human brain, computer networks and bank transactions to social interactions. Anomaly detection is an important data mining task consisting in detecting rare objects that deviate from the majority of the data. Contextual collective anomaly detection techniques can be applied to intrusion detection in computer networks, bank fraud detection, or finding people with strange behavior in social networks. In this work, a fast and intuitive algorithm to detect collective contextual anomalies is presented. Furthermore, the importance of selecting algorithms which find meaningful outliers for the application domain specialists is analyzed.